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5,371 results for “Flow”
Dataset for Viscous Dissipation and Apparent Permeability of Gas Flow in Nano-Porous Media
<p>Dataset for the Publication of 'Viscous Dissipation and Apparent Permeability of Gas Flow in Nano-Porous Media'.</p>
Multi-scale simulation of flow through a wind farm during a frontal passage event
<p>Animation from a multi-scale simulation of flow through a wind farm showing hub-height wind speed during a frontal passage event. The simulation was performed using the Weather Research and Forecasting (WRF) model using an embedded generalized actuator disk (GAD) wind turbine model. This animation is included as supplementary material for the manuscript "Multi-scale simulation of wind farm performance during a frontal passage" published in<em> <a href="https://www.mdpi.com/2073-4433/11/3/245#">Atmosphere</a></em><a href="https://www.mdpi.com/2073-4433/11/3/245#"> 2020 11(3), 245</a>, Special Issue "Modeling of Atmospheric Boundary Layers at Turbulence-Resolving Grid Spacings." This work was prepared by LLNL under Contract DE-AC52-07NA27344.</p>
The flow past a flatback airfoil with flow control devices: Benchmarking numerical simulations against wind tunnel data - Animations
<p>As wind turbines grow larger, the use of flatback airfoils has become standard practice for the root region of the blades. Flatback profiles provide higher lift and reduced sensitivity to soiling at significantly higher drag values. A number of flow control devices has been proposed to improve the performance of flatback profiles. In the present study, the flow past a flatback airfoil at a chord Reynolds number of 1.5×10<sup>6 </sup>with and without trailing edge flow control devices is considered. Two different numerical approaches are applied, Unsteady Reynolds Averaged Navier Stokes (RANS) simulations and Detached Eddy Simulations (DES). The computational predictions are compared to wind tunnel measurements to assess the suitability of each method. The effect of each flow control device on the flow is examined based on the DES results on the finer mesh. Results agree well with the experimental findings and show that a newly proposed flap device outperforms traditional solutions for flatback airfoils. In terms of numerical modelling, the more expensive DES approach is more suitable if the wake frequencies are of interest, but the simplest 2D RANS simulations can provide acceptable load predictions.</p> <p>These animations are the DES results on a Fine (25M cells, AR = 1) mesh. Animations include a 3D view of Q = 1.5 isosurfaces and a side view of Q = 100 isosurfaces for each case.</p> <p> </p>
Three-dimensional hydraulic tomography analysis of long-term municipal wellfield operations: Validation with synthetic flow and solute transport data
<p>Attached are the datasets utilized to accomplish the study of "<em>Three-dimensional hydraulic tomography analysis of long-term municipal wellfiled operations: Validation with synthetic flow and solute transport data". </em>Basically, it includes the information of the discrete model and assigned, wells as well as the generated synthetic hydraulic head and solute concentration data.</p>
On the Internal Flow at the Water Entry Cavity--data
<p>A set of raw PIV images are presented to support our findings, please contact the author for more resources.</p>
A Verification Technique for Real-Time Information Flow Security
<p>Evaluation artifacts accompanying the PhD thesis of Christopher Gerking, concerning the chapter on "A Verification Technique for Real-Time Information Flow Security".</p>
Contextualisation of Data Flow Diagrams for security analysis: Pilot study model
<p>CAIRIS model package to accompany 'Contextualisation of Data Flow Diagrams for security analysis' GraMSec 2020 paper. </p> <p>The model package can be loaded in CAIRIS via the System >> Import Model menu.</p>
The flow past a flatback airfoil with flow control devices: Benchmarking numerical simulations against wind tunnel data - Animations II
<p>Animations from accepted (06/2020) publication:</p> <p>The flow past a flatback airfoil with flow control devices: Benchmarking numerical simulations against wind tunnel data, Wind Energ. Sci., https://doi.org/10.5194/wes-2020-36</p> <p>These animations are the DES results on a Fine (25M cells, AR = 1) mesh. Animations include a 3D view of Delta = 10^5 isosurfaces coloured by X vorticity and Z vorticity. Two animations are available for each case (Plain airfoil, Flap, Flap + Cavity, Cavity, Splitter). Contour levels are included in separate files.</p> <p> </p>
Data file for paper: JJavier Rubio-Garcia, Junyi Cui, Andres Parra-Puerto and Anthony Kucernak "Hydrogen/Vanadium Hybrid Redox Flow Battery with Enhanced Electrolyte Concentration"
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Javier Rubio-Garcia, Junyi Cui, Andres Parra-Puerto and Anthony Kucernak, "Hydrogen/Vanadium Hybrid Redox Flow Battery with Enhanced Electrolyte Concentration",Energy Storage Materials, 2020<br> DOI: https://doi.org/10.1016/j.ensm.2020.05.031</p>
High flow nasal oxygen during procedural sedation: A randomized controlled trial
<p>Data and code for publication</p>
IoT-deNAT: Outbound flow-based network traffic data of IoT and non-IoT devices behind a home NAT
<p>This dataset is comprised of NetFlow records, which capture the outbound network traffic of 8 commercial IoT devices and 5 non-IoT devices, collected during a period of 37 days in a lab at Ben-Gurion University of The Negev. The dataset was collected in order to develop a method for telecommunication providers to detect vulnerable IoT models behind home NATs. Each NetFlow record is labeled with the device model which produced it; for research reproducibilty, each NetFlow is also allocated to either the "training" or "test" set, in accordance with the partitioning described in:</p> <p>Y. Meidan, V. Sachidananda, H. Peng, R. Sagron, Y. Elovici, and A. Shabtai, A novel approach for detecting vulnerable IoT devices connected behind a home NAT, Computers & Security, Volume 97, 2020, 101968, ISSN 0167-4048, https://doi.org/10.1016/j.cose.2020.101968. (http://www.sciencedirect.com/science/article/pii/S0167404820302418)</p> <p> </p> <p>Please note:</p> <ul> <li>The dataset itself is free to use, however users are requested to cite the above-mentioned paper, which describes in detail the research objectives as well as the data collection, preparation and analysis.</li> <li>Following is a brief description of the features used in this dataset.</li> </ul> <p> </p> <p># NetFlow features, used in the related paper for analysis</p> <p>'FIRST_SWITCHED': System uptime at which the first packet of this flow was switched<br> 'IN_BYTES': Incoming counter for the number of bytes associated with an IP Flow<br> 'IN_PKTS': Incoming counter for the number of packets associated with an IP Flow<br> 'IPV4_DST_ADDR': IPv4 destination address<br> 'L4_DST_PORT': TCP/UDP destination port number<br> 'L4_SRC_PORT': TCP/UDP source port number<br> 'LAST_SWITCHED': System uptime at which the last packet of this flow was switched<br> 'PROTOCOL': IP protocol byte (6: TCP, 17: UDP)<br> 'SRC_TOS': Type of Service byte setting when there is an incoming interface<br> 'TCP_FLAGS': Cumulative of all the TCP flags seen for this flow</p> <p> </p> <p># Features added by the authors</p> <p>'IP': Prefix of the destination IP address, representing the network (without the host)<br> 'DURATION': Time (seconds) between first/last packet switching</p> <p> </p> <p># Label<br> 'device_model': <type>.<manufacturer>.<model number></p> <p> </p> <p># Partition<br> 'partition': Training or test</p> <p> </p> <p># Additional NetFlow features (mostly zero-variance)<br> 'SRC_AS': Source BGP autonomous system number<br> 'DST_AS': Destination BGP autonomous system number<br> 'INPUT_SNMP': Input interface index<br> 'OUTPUT_SNMP': Output interface index<br> 'IPV4_SRC_ADDR': IPv4 source address<br> 'MAC': MAC address of the source</p> <p> </p> <p># Additional data<br> 'category': IoT or non-IoT<br> 'type': IoT, access_point, smartphone, laptop<br> 'date': Datepart of FIRST_SWITCHED<br> 'inter_arrival_time': Time (seconds) between successive flows of the same device (identified by its MAC address)</p>
256data-entrance-inclined-flow
<p>Training data for weakly supervised learning</p>
Dataset: Impact of tall buildings and building-height variability on flow within and above a complex urban canopy in London - Part II
<p>This dataset contains Part II of a series of wind-tunnel flow measurements in a 1:200 scale model of a part of the London Borough of Southwark. <a href="https://doi.org/10.5281/zenodo.2563700">Part I</a> of the dataset is also published on Zenodo. The data published here (Part II) accompanies analyses in <a href="https://doi.org/10.1039/D0FD00098A">Hertwig et al. (2020)</a>:</p> <p>D. Hertwig, S. Grimmond, S. Kotthaus, C. Vanderwel, H. Gough, M. Haefferlin, A. Robins (2020), "Variability of Physical Meteorology in Urban Areas at Different Scales: Implications for Air Quality", Faraday Discussions, DOI: 10.1039/D0FD00098A .</p> <p>Flow measurements (based on 2D Laser Doppler Anemometry) were carried out in the boundary-layer wind-tunnel facility of the Environmental Flow (EnFlo) Research Centre, University of Surrey, UK, using the EPSRC <a href="http://www.magic-air.uk/">MAGIC</a> project model domain and scale model (<a href="https://www.tandfonline.com/doi/full/10.1080/09613218.2018.1468158">Song et al., 2018</a>).</p> <p>This repository contains:</p> <ol> <li>wt_ts_part_II_readme.pdf: Meta information and description of data in 'wt_ts_part_II_data.zip'</li> <li>wt_ts_part_II_data.zip: Flow measurements (time series and statistics)</li> </ol>
Intermittency and critical scaling in annular Couette flow
<p>Time evolution of turbulent fraction <em>F<sub>t</sub></em>(<em>t</em>) and of fluctuating velocity fields visualized at mid-gap, for <em>h</em> = 0.1 with an artificially extended azimuthal domain size of <em><span class="math-tex">\(L_\theta = 128 \pi\)</span></em>. On the right column, contours show<br> <em>x</em>-<span class="math-tex">\(\theta\)</span> distributions of the radial velocity fluctuation <span class="math-tex">\(u_r'\)</span> normalized by the inner-cylinder velocity <em>U<sub>w</sub></em>. Top (orange box and curve in the graph) : above the global critical Reynolds number <em>Re<sub>g</sub></em>. Middle (red) : near <em>Re<sub>g</sub></em>. Bottom (black) : below <em>Re<sub>g</sub></em>.</p>
Migration flow and complex network
<p>Data is collected from Baidu Map (http://qianxi.baidu.com/). The migration scale index data, produced by the map service company of Baidu through the GPS location in mobile phone, record the number of migrants between any two cities. It visualises the migration flows in particular time periods. Although, it cannot capture all migrants, it is useful to conduct comparisons among different cities.</p>
Supplementary materials for "Mass flux density, momentum, and flow velocity"
<p>Supplementary materials for "Mass flux density, momentum, and flow velocity"</p> <p>MARUYAMA, Kiyoshi. (2015, June 29). Mass flux density, momentum, and flow velocity. Zenodo. http://doi.org/10.5281/zenodo.3924266</p>
S1_raw_images paper "Use of dual-flow bioreactor to develop a simplified model of nervous-cardiovascular systems crosstalk: a preliminary assessment
<p>Original uncropped and unadjusted images underlying all blot or gel results reported in a submission’s figures and Supporting Information files.</p>
Inverse estimation of spatiotemporal flux boundary conditions in unsaturated water flow modeling
<p>Data used in field case study in the paper titled "Inverse estimation of spatiotemporal flux boundary conditions in unsaturated water flow modeling".</p>
Figure 4 from: Sampaio FAC, Rufino MS, Pompeu PS, Santos HA, Ferreira RL (2020) Hydraulic flow resistance of epigean and hypogean fish of the family Trichomycteridae (Ostariophysi, Siluriformes). Subterranean Biology 35: 97-110. https://doi.org/10.3897/subtbiol.35.55064
Figure 4 Absolute critical speed (m/s) of the tested epigean and hypogean species.
Figure 1 from: Sampaio FAC, Rufino MS, Pompeu PS, Santos HA, Ferreira RL (2020) Hydraulic flow resistance of epigean and hypogean fish of the family Trichomycteridae (Ostariophysi, Siluriformes). Subterranean Biology 35: 97-110. https://doi.org/10.3897/subtbiol.35.55064
Figure 1 Number of specimens according to total length (cm) classes.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.